Android Malware Classification Using K-Means Clustering Algorithm

International Research and Innovation Summit 2017

Hamid, Isredza Rahmi A.^1 ; Khalid, Nur Syafiqah^1 ; Abdullah, Nurul Azma^1 ; Rahman, Nurul Hidayah Ab^1 ; Wen, Chuah Chai^1

Information Security Interest Group (ISIG), Faculty Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia^1

关键词: Android malware;    Data classification;    High-accuracy;    K-Means clustering algorithm;    Random forest algorithm;    Weka tool;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/226/1/012105/pdf
DOI  :  10.1088/1757-899X/226/1/012105

来源: IOP

【 摘 要 】

Malware was designed to gain access or damage a computer system without user notice. Besides, attacker exploits malware to commit crime or fraud. This paper proposed Android malware classification approach based on K-Means clustering algorithm. We evaluate the proposed model in terms of accuracy using machine learning algorithms. Two datasets were selected to demonstrate the practicing of K-Means clustering algorithms that are Virus Total and Malgenome dataset. We classify the Android malware into three clusters which are ransomware, scareware and goodware. Nine features were considered for each types of dataset such as Lock Detected, Text Detected, Text Score, Encryption Detected, Threat, Porn, Law, Copyright and Moneypak. We used IBM SPSS Statistic software for data classification and WEKA tools to evaluate the built cluster. The proposed K-Means clustering algorithm shows promising result with high accuracy when tested using Random Forest algorithm.